- Published
- 2022-06-21
- Venue
- Sensors, vol. 22, no. 13, pp. 4670
- Cited by
- 291
- Open access
- Yes, gold
- DOI
- 10.3390/s22134670
- PubMed
- 35808172
Abstract
A stroke is caused when blood flow to a part of the brain is stopped abruptly. Without the blood supply, the brain cells gradually die, and disability occurs depending on the area of the brain affected. Early recognition of symptoms can significantly carry valuable information for the prediction of stroke and promoting a healthy life. In this research work, with the aid of machine learning (ML), several models are developed and evaluated to design a robust framework for the long-term risk prediction of stroke occurrence. The main contribution of this study is a stacking method that achieves a high performance that is validated by various metrics, such as AUC, precision, recall, F-measure and accuracy. The experiment results showed that the stacking classification outperforms the other methods, with an AUC of 98.9%, F-measure, precision and recall of 97.4% and an accuracy of 98%.
Cite this paper
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- APA 7
Dritsas, E., & Τρίγκα, Μ. (2022). Stroke Risk Prediction with Machine Learning Techniques. Sensors, 22(13), 4670. https://doi.org/10.3390/s22134670
- MLA 9
Dritsas, Elias, and Μαρία Τρίγκα. "Stroke Risk Prediction with Machine Learning Techniques." Sensors, vol. 22, no. 13, 2022, pp. 4670, https://doi.org/10.3390/s22134670.
- Chicago
Dritsas, Elias, and Μαρία Τρίγκα. "Stroke Risk Prediction with Machine Learning Techniques." Sensors 22, no. 13 (2022): 4670. https://doi.org/10.3390/s22134670.
- GOST
Dritsas E., Τρίγκα Μ. Stroke Risk Prediction with Machine Learning Techniques // Sensors. — 2022. — Vol. 22, № 13. — P. 4670. — URL: https://doi.org/10.3390/s22134670
- BibTeX
@article{dritsas2022stroke, title = {{Stroke Risk Prediction with Machine Learning Techniques}}, author = {Elias Dritsas and Μαρία Τρίγκα}, journal = {Sensors}, year = {2022}, volume = {22}, number = {13}, pages = {4670}, doi = {10.3390/s22134670}, url = {https://doi.org/10.3390/s22134670} }- RIS
TY - JOUR AU - Dritsas, Elias AU - Τρίγκα, Μαρία TI - Stroke Risk Prediction with Machine Learning Techniques JO - Sensors VL - 22 IS - 13 SP - 4670 PY - 2022 DA - 2022/06/21 DO - 10.3390/s22134670 UR - https://doi.org/10.3390/s22134670 ER -
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